TGI vs 
SGLang
TGI vs SGLang compared for 2026 — features, license, ease of use, performance and which one to choose. Hugging Face's production text server vs Fast serving with structured outputs.
Updated regularly · curated by olud.ai
TGI vs SGLang at a glance
| Spec | TGI | SGLang |
|---|---|---|
| Category | Inference server | Inference server |
| Type | Inference server | Inference server |
| License | Apache-2.0 | Apache-2.0 |
| Runs locally | Self-hosted | Self-hosted |
| Primary language | Rust | Python |
| Ease of use | Advanced | Advanced |
| Best for | teams in the Hugging Face ecosystem | teams needing structured-output serving |
| GitHub stars | — | 30.6k |
Feature comparison
| Feature | TGI | SGLang |
|---|---|---|
| OpenAI-compatible API | ✓ | ✓ |
| Continuous batching | ✓ | ✓ |
| Quantization | ✓ | ✓ |
| Multi-GPU | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Docker | ✓ | ✓ |
How TGI and SGLang score
| Criterion | TGI | SGLang |
|---|---|---|
| Popularity | n/a | 4.0 |
| Maintenance | n/a | 5.0 |
| Ease of use | 2.5 | 2.5 |
| Privacy | 4.5 | 4.5 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
TGI
Text Generation Inference (TGI) is Hugging Face's production-grade server for deploying and serving LLMs, with continuous batching, quantization and tight Hub integration.
- Production-grade, battle-tested at Hugging Face
- Continuous batching and quantization built in
- Tight integration with the HF Hub
SGLang
SGLang is a fast serving framework for LLMs and vision-language models, featuring RadixAttention and strong support for structured and programmatic generation.
- Very fast with RadixAttention caching
- First-class structured / programmatic generation
- Strong vision-language model support
Key differences
TGI is inference server, while SGLang is inference server. In short, TGI fits teams in the Hugging Face ecosystem, and SGLang fits teams needing structured-output serving.
Which should you choose?
Choose TGI for teams in the Hugging Face ecosystem. Choose SGLang for teams needing structured-output serving.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is TGI or SGLang easier to use?
Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.
Are TGI and SGLang free?
TGI is free and open source (Apache-2.0), and SGLang is free and open source (Apache-2.0). Neither charges for the core software.
Can I run TGI and SGLang locally?
TGI: self-hosted · SGLang: self-hosted. Both can be used without sending your data to a third-party cloud where their setup allows.
TGI vs SGLang — which should I pick in 2026?
Choose TGI for teams in the Hugging Face ecosystem. Choose SGLang for teams needing structured-output serving.
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